Caught up in power: Exploring discursive frictions in community research
Bibliographic record
Abstract
This article outlines the debate around the emancipatory claims of community-based research (CBR) and identifies discursive frictions as a pivotal point upon which much of CBR practice revolves. Using a Foucauldian theoretical lens, we suggest that CBR is neither inherently emancipatory nor repressive, but that research outcomes are more often a product of power asymmetries in CBR relationships. To illustrate how power asymmetries in research relationships produce discursive frictions, several studies from our work and the literature are presented. The article provides examples of CBR relationships between the researcher and community members and relationships within the community to illustrate how power asymmetries and discursive frictions in these relationships dynamically influence research outcomes and thus alert researchers to the need to address power asymmetries not just before initiating CBR projects, but during CBR projects as well. We interrogate how power asymmetries and discursive frictions operate and are constructed in CBR in an attempt to highlight how research might be conducted more effectively and ethically. Finally, we indicate that some of the tensions and challenges associated with CBR might be ameliorated by the use of participatory facilitation methodologies, such as photo-voice and story circle discussion groups, that draw attention to power asymmetries and purposefully use more creative participatory tools to restructure power relationships and ultimately address the inequities that exist in the research process. Because CBR is continually caught up in power dynamics, we hope that highlighting some examples might offer an opportunity for increased dialogue and critical reflection on its claims of empowerment and emancipation.Keywords: discursive friction, Foucault, participatory methodologies, power asymmetries, research relationships, emancipatory research
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.027 | 0.097 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".